mlsanityData to 7 Oct 2026

Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning

arXiv 2201.02435 ↗First posted 7 Jan 2022cs.LG cs.AIcited by 32

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Abstract (arXiv)

Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, crime events are distributed unevenly on both spatial and temporal domains; ii) time-evolving dependencies between different types of crimes (e.g., Theft, Robbery, Assault, Damage) which reveal fine-grained semantics of crimes. To tackle these challenges, we propose Spatial-Temporal Sequential Hypergraph Network (ST-SHN) to collectively encode complex crime spatial-temporal patterns as well as the underlying category-wise crime semantic relationships. In specific, to handle spatial-temporal dynamics under the long-range and global context, we design a graph-structured message passing architecture with the integration of the hypergraph learning paradigm. To capture category-wise crime heterogeneous relations in a dynamic environment, we introduce a multi-channel routing mechanism to learn the time-evolving structural dependency across crime types. We conduct extensive experiments on two real-world datasets, showing that our proposed ST-SHN framework can significantly improve the prediction performance as compared to various state-of-the-art baselines. The source code is available at: https://github.com/akaxlh/ST-SHN.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 9 of its 34 references that are in the map. First posted on arXiv 7 Jan 2022.

Builds on

9 of 34 references.

References · 9

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  3. 2019Heterogeneous Graph Attention Networkcited by 732
  4. 2020Heterogeneous Graph Transformercited by 581
  5. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 433
  6. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 330
  7. 2018Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Predictioncited by 119
  8. 2021Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Networkcited by 52
  9. 2020Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Modelcited by 22

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 8; 2024: 16; 2025: 3; 2026: 1.

32 papers.

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  1. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  2. 2022Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Predictioncited by 39
  3. 2024Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlookcited by 33
  4. 2022Multi-Graph Fusion Networks for Urban Region Embeddingcited by 32
  5. 2024OpenCity: Open Spatio-Temporal Foundation Models for Traffic Predictioncited by 27
  6. 2023Automated Spatio-Temporal Graph Contrastive Learningcited by 24
  7. 2022Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challengescited by 15
  8. 2023Dynamic Graph Representation Learning with Neural Networks: A Surveycited by 15
  9. 2024A Survey of Generative Techniques for Spatial-Temporal Data Miningcited by 14
  10. 2023Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptationcited by 13
  11. 2023Explainable Spatio-Temporal Graph Neural Networkscited by 13
  12. 2023Road Network Representation Learning: A Dual Graph based Approachcited by 12
20 more
  1. 2023Spatio-Temporal Meta Contrastive Learningcited by 11
  2. 2024A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classificationcited by 9
  3. 2022Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropoutcited by 7
  4. 2024Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenariocited by 7
  5. 2024EasyST: A Simple Framework for Spatio-Temporal Predictioncited by 6
  6. 2024Uncertainty-Aware Crime Prediction With Spatial Temporal Multivariate Graph Neural Networkscited by 5
  7. 2024Spatial-Temporal Mixture-of-Graph-Experts for Multi-Type Crime Predictioncited by 3
  8. 2024STEMO: Early Spatio-temporal Forecasting with Multi-Objective Reinforcement Learningcited by 2
  9. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  10. 2024HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learningcited by 2
  11. 2025Urban Computing in the Era of Large Language Modelscited by 2
  12. 2023Spatiotemporal and Semantic Zero-inflated Urban Anomaly Predictioncited by 1
  13. 2024Empirical and Experimental Insights into Data Mining Techniques for Crime Prediction: A Comprehensive Surveycited by 0
  14. 2024SMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for Traffic Accident Predictioncited by 0
  15. 2024Deep Learning Based Crime Prediction Models: Experiments and Analysiscited by 0
  16. 2024GINTRIP: Interpretable Temporal Graph Regression using Information bottleneck and Prototype-based methodcited by 0
  17. 2024An Event-centric Framework for Predicting Crime Hotspots with Flexible Time Intervalscited by 0
  18. 2025Deep Learning for Crime Forecasting: The Role of Mobility at Fine-grained Spatiotemporal Scalescited by 0
  19. 2025Learning A Universal Crime Predictor with Knowledge-guided Hypernetworkscited by 0
  20. 2026Frequency-Domain Multi-Modality Transportation Modelingcited by 0